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research / researchAI in radiology and computer-aided detection

Ronald Summers

罗纳德·萨默斯

MD, PhD

🏢National Institutes of Health Clinical Center(美国国立卫生研究院临床中心)🌐USA

Senior Investigator, Radiology and Imaging Sciences放射学与影像科学高级研究员

60
h-index
2
Key Papers
3
Awards
2
Key Contributions

👥Biography 个人简介

Ronald Summers is a pioneering researcher in AI and computer-aided detection for cancer imaging who has made seminal contributions to CT colonography polyp detection and AI-driven body composition analysis. His laboratory developed automated measurement of lymph node size, body composition, and lesion characterization from CT imaging, providing tools widely used in oncology research and clinical practice. Summers has built large annotated CT imaging datasets at NIH that have powered deep learning advances in radiology, and has developed AI systems for opportunistic cancer screening from routinely acquired CT scans. His work bridges computer vision, radiology, and oncology.

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🧪Research Fields 研究领域

AI radiology放射学AI
computer-aided detection计算机辅助检测
CT colonographyCT结肠镜
deep learning imaging深度学习成像
large image datasets大型影像数据集

🎓Key Contributions 主要贡献

CT Colonography AI

Developed and validated AI-powered computer-aided detection for CT colonography polyp detection, contributing to adoption of this non-invasive colorectal cancer screening approach.

Opportunistic CT Screening

Demonstrated that AI analysis of CT scans performed for any indication can extract body composition data (muscle, fat, bone) and detect incidental findings that predict cancer risk, enabling opportunistic population screening.

Representative Works 代表性著作

[1]

Automated Detection of Colorectal Polyps at CT Colonography

Radiology (2001)

Early CAD validation for CT colonography polyp detection.

[2]

Opportunistic Screening for Body Composition Using CT Examinations

Radiology (2021)

AI-driven body composition analysis from routine CT scans.

🏆Awards & Recognition 奖项与荣誉

🏆RSNA Honored Educator Award
🏆NCI Director Achievement Award
🏆MICCAI Society Recognition Award

📄Data Sources 数据来源

Institution

Last updated: 2026-01-15 | All information from publicly available academic sources

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